Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations5634146
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory644.8 MiB
Average record size in memory120.0 B

Variable types

DateTime1
Numeric12
Text1
Categorical1

Alerts

승차인원 is highly overall correlated with 승하차인원 and 3 other fieldsHigh correlation
승하차인원 is highly overall correlated with 승차인원 and 3 other fieldsHigh correlation
우대권인원수 is highly overall correlated with 승차인원 and 3 other fieldsHigh correlation
하차인원 is highly overall correlated with 승차인원 and 3 other fieldsHigh correlation
혼잡도 is highly overall correlated with 승차인원 and 3 other fieldsHigh correlation
시간 is uniformly distributedUniform
강수량(mm) has 5278699 (93.7%) zerosZeros
적설(cm) has 5401650 (95.9%) zerosZeros

Reproduction

Analysis started2024-09-23 14:26:43.568139
Analysis finished2024-09-23 14:29:18.213075
Duration2 minutes and 34.64 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

날짜
Date

Distinct1095
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.0 MiB
Minimum2021-01-01 00:00:00
Maximum2023-12-31 00:00:00
2024-09-23T23:29:18.274634image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:29:18.352759image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

호선명
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6113565
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 MiB
2024-09-23T23:29:18.416162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9999853
Coefficient of variation (CV)0.43370868
Kurtosis-1.1432409
Mean4.6113565
Median Absolute Deviation (MAD)2
Skewness-0.057862012
Sum25981056
Variance3.9999413
MonotonicityNot monotonic
2024-09-23T23:29:18.478247image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 1160387
20.6%
2 1019445
18.1%
7 873810
15.5%
6 772673
13.7%
3 691011
12.3%
4 540930
9.6%
8 367840
 
6.5%
1 208050
 
3.7%
ValueCountFrequency (%)
1 208050
 
3.7%
2 1019445
18.1%
3 691011
12.3%
4 540930
9.6%
5 1160387
20.6%
6 772673
13.7%
7 873810
15.5%
8 367840
 
6.5%
ValueCountFrequency (%)
8 367840
 
6.5%
7 873810
15.5%
6 772673
13.7%
5 1160387
20.6%
4 540930
9.6%
3 691011
12.3%
2 1019445
18.1%
1 208050
 
3.7%

역명
Text

Distinct239
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.0 MiB
2024-09-23T23:29:18.702823image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.8959951
Min length2

Characters and Unicode

Total characters16316459
Distinct characters207
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울역
2nd row서울역
3rd row서울역
4th row서울역
5th row서울역
ValueCountFrequency (%)
종로3가 62415
 
1.1%
동대문역사문화공원 62415
 
1.1%
시청 41610
 
0.7%
동대문 41610
 
0.7%
건대입구 41610
 
0.7%
동묘앞 41610
 
0.7%
을지로4가 41610
 
0.7%
을지로3가 41610
 
0.7%
왕십리 41610
 
0.7%
신당 41610
 
0.7%
Other values (229) 5176436
91.9%
2024-09-23T23:29:19.004475image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
665760
 
4.1%
540930
 
3.3%
460598
 
2.8%
457710
 
2.8%
393699
 
2.4%
312075
 
1.9%
296305
 
1.8%
291270
 
1.8%
291270
 
1.8%
289693
 
1.8%
Other values (197) 12317149
75.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16150019
99.0%
Decimal Number 166440
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
665760
 
4.1%
540930
 
3.3%
460598
 
2.9%
457710
 
2.8%
393699
 
2.4%
312075
 
1.9%
296305
 
1.8%
291270
 
1.8%
291270
 
1.8%
289693
 
1.8%
Other values (194) 12150709
75.2%
Decimal Number
ValueCountFrequency (%)
3 104025
62.5%
4 41610
 
25.0%
5 20805
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16150019
99.0%
Common 166440
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
665760
 
4.1%
540930
 
3.3%
460598
 
2.9%
457710
 
2.8%
393699
 
2.4%
312075
 
1.9%
296305
 
1.8%
291270
 
1.8%
291270
 
1.8%
289693
 
1.8%
Other values (194) 12150709
75.2%
Common
ValueCountFrequency (%)
3 104025
62.5%
4 41610
 
25.0%
5 20805
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16150019
99.0%
ASCII 166440
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
665760
 
4.1%
540930
 
3.3%
460598
 
2.9%
457710
 
2.8%
393699
 
2.4%
312075
 
1.9%
296305
 
1.8%
291270
 
1.8%
291270
 
1.8%
289693
 
1.8%
Other values (194) 12150709
75.2%
ASCII
ValueCountFrequency (%)
3 104025
62.5%
4 41610
 
25.0%
5 20805
 
12.5%

시간
Categorical

UNIFORM 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.0 MiB
05:00
 
296534
06:00
 
296534
07:00
 
296534
08:00
 
296534
09:00
 
296534
Other values (14)
4151476 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters28170730
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row05:00
2nd row06:00
3rd row07:00
4th row08:00
5th row09:00

Common Values

ValueCountFrequency (%)
05:00 296534
 
5.3%
06:00 296534
 
5.3%
07:00 296534
 
5.3%
08:00 296534
 
5.3%
09:00 296534
 
5.3%
10:00 296534
 
5.3%
11:00 296534
 
5.3%
12:00 296534
 
5.3%
13:00 296534
 
5.3%
14:00 296534
 
5.3%
Other values (9) 2668806
47.4%

Length

2024-09-23T23:29:19.085490image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
05:00 296534
 
5.3%
15:00 296534
 
5.3%
22:00 296534
 
5.3%
21:00 296534
 
5.3%
20:00 296534
 
5.3%
19:00 296534
 
5.3%
18:00 296534
 
5.3%
17:00 296534
 
5.3%
16:00 296534
 
5.3%
14:00 296534
 
5.3%
Other values (9) 2668806
47.4%

Most occurring characters

ValueCountFrequency (%)
0 13344030
47.4%
: 5634146
20.0%
1 3558408
 
12.6%
2 1779204
 
6.3%
5 593068
 
2.1%
6 593068
 
2.1%
7 593068
 
2.1%
8 593068
 
2.1%
9 593068
 
2.1%
3 593068
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22536584
80.0%
Other Punctuation 5634146
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13344030
59.2%
1 3558408
 
15.8%
2 1779204
 
7.9%
5 593068
 
2.6%
6 593068
 
2.6%
7 593068
 
2.6%
8 593068
 
2.6%
9 593068
 
2.6%
3 593068
 
2.6%
4 296534
 
1.3%
Other Punctuation
ValueCountFrequency (%)
: 5634146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28170730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13344030
47.4%
: 5634146
20.0%
1 3558408
 
12.6%
2 1779204
 
6.3%
5 593068
 
2.1%
6 593068
 
2.1%
7 593068
 
2.1%
8 593068
 
2.1%
9 593068
 
2.1%
3 593068
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28170730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13344030
47.4%
: 5634146
20.0%
1 3558408
 
12.6%
2 1779204
 
6.3%
5 593068
 
2.1%
6 593068
 
2.1%
7 593068
 
2.1%
8 593068
 
2.1%
9 593068
 
2.1%
3 593068
 
2.1%

승차인원
Real number (ℝ)

HIGH CORRELATION 

Distinct13260
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean743.62836
Minimum0
Maximum21209
Zeros8271
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.0 MiB
2024-09-23T23:29:19.156229image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54
Q1212
median450
Q3880
95-th percentile2398
Maximum21209
Range21209
Interquartile range (IQR)668

Descriptive statistics

Standard deviation994.54444
Coefficient of variation (CV)1.3374213
Kurtosis34.532946
Mean743.62836
Median Absolute Deviation (MAD)287
Skewness4.5942021
Sum4.1897107 × 109
Variance989118.64
MonotonicityNot monotonic
2024-09-23T23:29:19.235477image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8271
 
0.1%
62 7590
 
0.1%
98 7430
 
0.1%
119 7416
 
0.1%
65 7402
 
0.1%
90 7400
 
0.1%
110 7395
 
0.1%
163 7391
 
0.1%
66 7388
 
0.1%
118 7383
 
0.1%
Other values (13250) 5559080
98.7%
ValueCountFrequency (%)
0 8271
0.1%
1 540
 
< 0.1%
2 859
 
< 0.1%
3 1258
 
< 0.1%
4 1580
 
< 0.1%
5 1832
 
< 0.1%
6 2078
 
< 0.1%
7 2430
 
< 0.1%
8 2773
 
< 0.1%
9 2888
 
0.1%
ValueCountFrequency (%)
21209 1
< 0.1%
20222 1
< 0.1%
19883 1
< 0.1%
18966 1
< 0.1%
18875 1
< 0.1%
18757 1
< 0.1%
18616 1
< 0.1%
16442 1
< 0.1%
15718 1
< 0.1%
15533 1
< 0.1%

하차인원
Real number (ℝ)

HIGH CORRELATION 

Distinct15344
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean744.13236
Minimum0
Maximum19267
Zeros10894
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size43.0 MiB
2024-09-23T23:29:19.312522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46
Q1221
median454
Q3862
95-th percentile2313
Maximum19267
Range19267
Interquartile range (IQR)641

Descriptive statistics

Standard deviation1044.4823
Coefficient of variation (CV)1.4036244
Kurtosis51.045525
Mean744.13236
Median Absolute Deviation (MAD)281
Skewness5.5535684
Sum4.1925504 × 109
Variance1090943.3
MonotonicityNot monotonic
2024-09-23T23:29:19.399523image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10894
 
0.2%
32 7613
 
0.1%
30 7562
 
0.1%
34 7538
 
0.1%
31 7451
 
0.1%
29 7370
 
0.1%
33 7353
 
0.1%
27 7339
 
0.1%
28 7318
 
0.1%
35 7302
 
0.1%
Other values (15334) 5556406
98.6%
ValueCountFrequency (%)
0 10894
0.2%
1 1878
 
< 0.1%
2 1959
 
< 0.1%
3 2437
 
< 0.1%
4 2915
 
0.1%
5 3492
 
0.1%
6 3816
 
0.1%
7 4162
 
0.1%
8 4419
0.1%
9 4771
0.1%
ValueCountFrequency (%)
19267 1
 
< 0.1%
19228 1
 
< 0.1%
19193 1
 
< 0.1%
19157 1
 
< 0.1%
19141 1
 
< 0.1%
19095 1
 
< 0.1%
19085 3
< 0.1%
19060 1
 
< 0.1%
19046 1
 
< 0.1%
19038 2
< 0.1%

우대권인원수
Real number (ℝ)

HIGH CORRELATION 

Distinct2926
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.14156
Minimum0
Maximum4350
Zeros10656
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size43.0 MiB
2024-09-23T23:29:19.484551image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q178
median175
Q3326
95-th percentile705
Maximum4350
Range4350
Interquartile range (IQR)248

Descriptive statistics

Standard deviation248.97393
Coefficient of variation (CV)1.0197933
Kurtosis12.227467
Mean244.14156
Median Absolute Deviation (MAD)113
Skewness2.7093094
Sum1.3755292 × 109
Variance61988.017
MonotonicityNot monotonic
2024-09-23T23:29:19.562069image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 20183
 
0.4%
23 20120
 
0.4%
41 19910
 
0.4%
24 19899
 
0.4%
22 19882
 
0.4%
20 19839
 
0.4%
27 19832
 
0.4%
29 19816
 
0.4%
44 19812
 
0.4%
45 19799
 
0.4%
Other values (2916) 5435054
96.5%
ValueCountFrequency (%)
0 10656
0.2%
1 5374
 
0.1%
2 7546
0.1%
3 9638
0.2%
4 11496
0.2%
5 12732
0.2%
6 13672
0.2%
7 14587
0.3%
8 15682
0.3%
9 16157
0.3%
ValueCountFrequency (%)
4350 1
< 0.1%
4181 1
< 0.1%
4150 1
< 0.1%
4043 1
< 0.1%
4017 1
< 0.1%
3936 1
< 0.1%
3743 1
< 0.1%
3737 1
< 0.1%
3674 1
< 0.1%
3659 1
< 0.1%

기온(°C)
Real number (ℝ)

Distinct531
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.284394
Minimum-18.5
Maximum36.3
Zeros8390
Zeros (%)0.1%
Negative736139
Negative (%)13.1%
Memory size43.0 MiB
2024-09-23T23:29:19.636975image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-18.5
5-th percentile-5.1
Q15.8
median15.6
Q323.7
95-th percentile29.7
Maximum36.3
Range54.8
Interquartile range (IQR)17.9

Descriptive statistics

Standard deviation11.102127
Coefficient of variation (CV)0.7772207
Kurtosis-0.79382046
Mean14.284394
Median Absolute Deviation (MAD)8.7
Skewness-0.37268836
Sum80480363
Variance123.25722
MonotonicityNot monotonic
2024-09-23T23:29:19.712256image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.1 29278
 
0.5%
25.2 25211
 
0.4%
25.6 25208
 
0.4%
19.7 25198
 
0.4%
25.4 24392
 
0.4%
24.2 24380
 
0.4%
23.5 23851
 
0.4%
24.8 23587
 
0.4%
24.9 23585
 
0.4%
25.1 23320
 
0.4%
Other values (521) 5386136
95.6%
ValueCountFrequency (%)
-18.5 267
 
< 0.1%
-18.2 267
 
< 0.1%
-17.9 267
 
< 0.1%
-17.8 534
< 0.1%
-17.2 271
 
< 0.1%
-17 271
 
< 0.1%
-16.9 271
 
< 0.1%
-16.8 542
< 0.1%
-16.6 542
< 0.1%
-16.4 1077
< 0.1%
ValueCountFrequency (%)
36.3 270
 
< 0.1%
36.1 270
 
< 0.1%
36 270
 
< 0.1%
35.8 271
 
< 0.1%
35.7 1082
< 0.1%
35.6 814
< 0.1%
35.4 813
< 0.1%
35.3 812
< 0.1%
35.2 1895
< 0.1%
35.1 1081
< 0.1%

강수량(mm)
Real number (ℝ)

ZEROS 

Distinct157
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17815206
Minimum0
Maximum64.7
Zeros5278699
Zeros (%)93.7%
Negative0
Negative (%)0.0%
Memory size43.0 MiB
2024-09-23T23:29:19.790390image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.3
Maximum64.7
Range64.7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4122468
Coefficient of variation (CV)7.9271987
Kurtosis411.62461
Mean0.17815206
Median Absolute Deviation (MAD)0
Skewness16.400728
Sum1003734.7
Variance1.9944409
MonotonicityNot monotonic
2024-09-23T23:29:19.866929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5278699
93.7%
0.1 42823
 
0.8%
0.2 30876
 
0.5%
0.3 23821
 
0.4%
0.5 16529
 
0.3%
0.4 14087
 
0.3%
0.7 11368
 
0.2%
1.3 10832
 
0.2%
0.6 10020
 
0.2%
1 9482
 
0.2%
Other values (147) 185609
 
3.3%
ValueCountFrequency (%)
0 5278699
93.7%
0.1 42823
 
0.8%
0.2 30876
 
0.5%
0.3 23821
 
0.4%
0.4 14087
 
0.3%
0.5 16529
 
0.3%
0.6 10020
 
0.2%
0.7 11368
 
0.2%
0.8 8664
 
0.2%
0.9 7050
 
0.1%
ValueCountFrequency (%)
64.7 271
< 0.1%
40.7 272
< 0.1%
34.7 271
< 0.1%
31.4 272
< 0.1%
31.3 271
< 0.1%
30.2 542
< 0.1%
29.5 271
< 0.1%
28 271
< 0.1%
27.7 271
< 0.1%
27.6 271
< 0.1%

풍속(m/s)
Real number (ℝ)

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.434749
Minimum0
Maximum8.6
Zeros13004
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size43.0 MiB
2024-09-23T23:29:19.946930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8
Q11.7
median2.3
Q33.1
95-th percentile4.5
Maximum8.6
Range8.6
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.1196801
Coefficient of variation (CV)0.45987493
Kurtosis1.0443184
Mean2.434749
Median Absolute Deviation (MAD)0.7
Skewness0.7443697
Sum13717732
Variance1.2536834
MonotonicityNot monotonic
2024-09-23T23:29:20.021670image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3 229986
 
4.1%
2.2 228061
 
4.0%
2.4 223443
 
4.0%
2 222123
 
3.9%
2.5 215328
 
3.8%
2.1 212321
 
3.8%
1.9 205572
 
3.6%
2.6 204225
 
3.6%
1.8 201467
 
3.6%
1.7 193598
 
3.4%
Other values (74) 3498022
62.1%
ValueCountFrequency (%)
0 13004
 
0.2%
0.1 11102
 
0.2%
0.2 13271
 
0.2%
0.3 20589
 
0.4%
0.4 28714
 
0.5%
0.5 35186
0.6%
0.6 50384
0.9%
0.7 60374
1.1%
0.8 72293
1.3%
0.9 85301
1.5%
ValueCountFrequency (%)
8.6 272
 
< 0.1%
8.5 272
 
< 0.1%
8.4 272
 
< 0.1%
8.3 270
 
< 0.1%
8.1 271
 
< 0.1%
8 813
< 0.1%
7.9 273
 
< 0.1%
7.6 542
< 0.1%
7.5 271
 
< 0.1%
7.4 1080
< 0.1%

습도(%)
Real number (ℝ)

Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.640826
Minimum15
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 MiB
2024-09-23T23:29:20.096864image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile32
Q149
median64
Q379
95-th percentile94
Maximum100
Range85
Interquartile range (IQR)30

Descriptive statistics

Standard deviation19.057644
Coefficient of variation (CV)0.29945627
Kurtosis-0.84181871
Mean63.640826
Median Absolute Deviation (MAD)15
Skewness-0.085536259
Sum3.585617 × 108
Variance363.19381
MonotonicityNot monotonic
2024-09-23T23:29:20.174042image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 112150
 
2.0%
70 103193
 
1.8%
58 102651
 
1.8%
67 102116
 
1.8%
61 102100
 
1.8%
54 101557
 
1.8%
66 101056
 
1.8%
63 100215
 
1.8%
71 98317
 
1.7%
62 97769
 
1.7%
Other values (76) 4613022
81.9%
ValueCountFrequency (%)
15 813
 
< 0.1%
16 542
 
< 0.1%
17 3257
 
0.1%
18 3524
 
0.1%
19 6239
0.1%
20 7033
0.1%
21 10284
0.2%
22 10582
0.2%
23 11647
0.2%
24 12705
0.2%
ValueCountFrequency (%)
100 22753
 
0.4%
99 40645
0.7%
98 43077
0.8%
97 47420
0.8%
96 52560
0.9%
95 49560
0.9%
94 55235
1.0%
93 61451
1.1%
92 67963
1.2%
91 71996
1.3%

적설(cm)
Real number (ℝ)

ZEROS 

Distinct66
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061164656
Minimum0
Maximum12.2
Zeros5401650
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size43.0 MiB
2024-09-23T23:29:20.252576image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12.2
Range12.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44190634
Coefficient of variation (CV)7.2248643
Kurtosis246.45201
Mean0.061164656
Median Absolute Deviation (MAD)0
Skewness13.065017
Sum344610.6
Variance0.19528121
MonotonicityNot monotonic
2024-09-23T23:29:20.337580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5401650
95.9%
0.3 17847
 
0.3%
0.2 17839
 
0.3%
0.4 14842
 
0.3%
0.5 14342
 
0.3%
0.1 12446
 
0.2%
1.1 9703
 
0.2%
0.7 9668
 
0.2%
1.7 9254
 
0.2%
1.9 8962
 
0.2%
Other values (56) 117593
 
2.1%
ValueCountFrequency (%)
0 5401650
95.9%
0.1 12446
 
0.2%
0.2 17839
 
0.3%
0.3 17847
 
0.3%
0.4 14842
 
0.3%
0.5 14342
 
0.3%
0.6 5129
 
0.1%
0.7 9668
 
0.2%
0.8 7526
 
0.1%
0.9 6189
 
0.1%
ValueCountFrequency (%)
12.2 271
< 0.1%
12 271
< 0.1%
11.9 271
< 0.1%
11.5 271
< 0.1%
11.4 271
< 0.1%
11.3 271
< 0.1%
10.8 271
< 0.1%
10.5 271
< 0.1%
10.2 271
< 0.1%
9.8 271
< 0.1%

면적
Real number (ℝ)

Distinct272
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8825.8212
Minimum1069.48
Maximum28768.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.0 MiB
2024-09-23T23:29:20.415992image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1069.48
5-th percentile5067.31
Q16515.21
median8150.87
Q310107.22
95-th percentile15318.58
Maximum28768.4
Range27698.92
Interquartile range (IQR)3592.01

Descriptive statistics

Standard deviation3466.7103
Coefficient of variation (CV)0.39279181
Kurtosis5.1572082
Mean8825.8212
Median Absolute Deviation (MAD)1721.87
Skewness1.6629479
Sum4.9725965 × 1010
Variance12018080
MonotonicityNot monotonic
2024-09-23T23:29:20.492970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6439 41610
 
0.7%
6086 41610
 
0.7%
10805 20805
 
0.4%
11066.23 20805
 
0.4%
6193.76 20805
 
0.4%
11229.45 20805
 
0.4%
5520.83 20805
 
0.4%
8423.57 20805
 
0.4%
12286.19 20805
 
0.4%
6990.22 20805
 
0.4%
Other values (262) 5384486
95.6%
ValueCountFrequency (%)
1069.48 779
 
< 0.1%
1423 20805
0.4%
1503.05 20805
0.4%
1583 20805
0.4%
2203 20805
0.4%
3860 5035
 
0.1%
4177.54 14155
0.3%
4496.94 20805
0.4%
4691 20805
0.4%
4838.6 20805
0.4%
ValueCountFrequency (%)
28768.4 20805
0.4%
23052.81 20805
0.4%
20302.8 20805
0.4%
19246 20805
0.4%
18984.55 20805
0.4%
18812.65 20805
0.4%
18506 20805
0.4%
18459.41 20805
0.4%
18195.21 20805
0.4%
17268.9 20805
0.4%

승하차인원
Real number (ℝ)

HIGH CORRELATION 

Distinct19024
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1487.7607
Minimum0
Maximum26156
Zeros8080
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.0 MiB
2024-09-23T23:29:20.574137image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile140
Q1488
median971
Q31818
95-th percentile4535
Maximum26156
Range26156
Interquartile range (IQR)1330

Descriptive statistics

Standard deviation1720.0265
Coefficient of variation (CV)1.1561177
Kurtosis19.924664
Mean1487.7607
Median Absolute Deviation (MAD)579
Skewness3.5627684
Sum8.3822611 × 109
Variance2958491
MonotonicityNot monotonic
2024-09-23T23:29:20.656599image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8080
 
0.1%
463 3559
 
0.1%
441 3504
 
0.1%
470 3461
 
0.1%
312 3461
 
0.1%
471 3459
 
0.1%
443 3456
 
0.1%
440 3445
 
0.1%
452 3442
 
0.1%
482 3442
 
0.1%
Other values (19014) 5594837
99.3%
ValueCountFrequency (%)
0 8080
0.1%
1 10
 
< 0.1%
2 15
 
< 0.1%
3 45
 
< 0.1%
4 53
 
< 0.1%
5 74
 
< 0.1%
6 126
 
< 0.1%
7 144
 
< 0.1%
8 176
 
< 0.1%
9 207
 
< 0.1%
ValueCountFrequency (%)
26156 1
< 0.1%
26041 1
< 0.1%
25366 1
< 0.1%
25099 1
< 0.1%
24551 1
< 0.1%
24522 1
< 0.1%
24415 1
< 0.1%
24323 1
< 0.1%
24261 1
< 0.1%
24123 1
< 0.1%

혼잡도
Real number (ℝ)

HIGH CORRELATION 

Distinct943058
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.482558
Minimum0
Maximum1190.5194
Zeros8080
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.0 MiB
2024-09-23T23:29:20.743614image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.9711944
Q117.519552
median35.522194
Q368.418704
95-th percentile172.45727
Maximum1190.5194
Range1190.5194
Interquartile range (IQR)50.899153

Descriptive statistics

Standard deviation66.839741
Coefficient of variation (CV)1.2046983
Kurtosis28.331733
Mean55.482558
Median Absolute Deviation (MAD)21.805988
Skewness4.0592238
Sum3.1259683 × 108
Variance4467.551
MonotonicityNot monotonic
2024-09-23T23:29:20.829150image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8080
 
0.1%
4.830759119 210
 
< 0.1%
4.534998357 202
 
< 0.1%
5.225106802 201
 
< 0.1%
5.320197044 194
 
< 0.1%
4.633585278 193
 
< 0.1%
4.876847291 193
 
< 0.1%
5.615763547 193
 
< 0.1%
4.436411436 192
 
< 0.1%
5.172413793 192
 
< 0.1%
Other values (943048) 5624296
99.8%
ValueCountFrequency (%)
0 8080
0.1%
0.02388344877 1
 
< 0.1%
0.02407434157 1
 
< 0.1%
0.02947189315 6
 
< 0.1%
0.0492934604 1
 
< 0.1%
0.0521405431 1
 
< 0.1%
0.05220691689 3
 
< 0.1%
0.05694674341 2
 
< 0.1%
0.05894378629 2
 
< 0.1%
0.06256865172 1
 
< 0.1%
ValueCountFrequency (%)
1190.519399 1
< 0.1%
1152.268461 1
< 0.1%
1150.907384 1
< 0.1%
1145.885482 1
< 0.1%
1138.657697 1
< 0.1%
1132.180851 1
< 0.1%
1123.545056 1
< 0.1%
1116.458073 1
< 0.1%
1111.013767 1
< 0.1%
1109.934293 1
< 0.1%

Interactions

2024-09-23T23:29:04.126237image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:00.693813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:06.342843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:12.134289image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:17.985615image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:23.762964image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:29.705339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:35.441476image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:41.235515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:47.033892image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:52.625723image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:58.281798image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:29:04.615518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:01.180549image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:06.825394image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:12.616300image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:18.464666image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:24.264905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:30.178817image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:35.922668image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:41.708576image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:47.521660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:53.109955image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:58.764302image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:29:05.094341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:01.649791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:07.325782image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:13.104876image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:18.932231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:24.751675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:30.649866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:36.402200image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:42.193196image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:47.980285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:53.611603image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:59.255886image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:29:05.623329image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:02.117371image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:07.808833image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:13.599834image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:19.430820image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:25.300784image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:31.143441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:36.902855image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:42.686921image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:48.452110image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:54.090033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:59.765021image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:29:06.118901image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:02.571209image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:08.292303image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:14.092282image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:19.910780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:25.788855image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:31.621041image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:37.386045image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:43.172508image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:48.903872image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:54.560243image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:29:00.273598image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:29:06.594514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:03.028713image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:08.758198image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:14.577950image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:20.391391image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:26.259293image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:32.082633image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:37.913696image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:43.641617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:49.360603image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:55.013041image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:29:00.747806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:29:07.068380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:03.491135image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:09.242185image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:15.059527image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:20.864635image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:26.739835image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:32.535713image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:38.378605image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:44.192965image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:49.823427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:55.474548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:29:01.229815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:29:07.566936image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:03.987554image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-09-23T23:28:09.717963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-09-23T23:29:03.640234image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2024-09-23T23:29:20.894808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
강수량(mm)기온(°C)면적습도(%)승차인원승하차인원시간우대권인원수적설(cm)풍속(m/s)하차인원호선명혼잡도
강수량(mm)1.0000.0930.0000.375-0.009-0.0110.030-0.014-0.0080.035-0.0120.000-0.011
기온(°C)0.0931.0000.0010.1570.0740.0710.0930.120-0.3130.0140.0820.0000.069
면적0.0000.0011.0000.0010.0640.0740.000-0.006-0.001-0.0000.0610.085-0.250
습도(%)0.3750.1570.0011.000-0.116-0.1140.172-0.1690.017-0.244-0.1390.000-0.111
승차인원-0.0090.0740.064-0.1161.0000.9080.0940.750-0.0160.1120.687-0.1980.843
승하차인원-0.0110.0710.074-0.1140.9081.0000.1150.756-0.0170.1100.909-0.2210.929
시간0.0300.0930.0000.1720.0940.1151.0000.1160.0310.1220.1000.0000.102
우대권인원수-0.0140.120-0.006-0.1690.7500.7560.1161.000-0.0280.1510.679-0.1680.725
적설(cm)-0.008-0.313-0.0010.017-0.016-0.0170.031-0.0281.0000.040-0.018-0.000-0.016
풍속(m/s)0.0350.014-0.000-0.2440.1120.1100.1220.1510.0401.0000.1220.0000.107
하차인원-0.0120.0820.061-0.1390.6870.9090.1000.679-0.0180.1221.000-0.2010.846
호선명0.0000.0000.0850.000-0.198-0.2210.000-0.168-0.0000.000-0.2011.000-0.250
혼잡도-0.0110.069-0.250-0.1110.8430.9290.1020.725-0.0160.1070.846-0.2501.000

Missing values

2024-09-23T23:29:09.859280image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-23T23:29:12.538801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

날짜호선명역명시간승차인원하차인원우대권인원수기온(°C)강수량(mm)풍속(m/s)습도(%)적설(cm)면적승하차인원혼잡도
02021-01-011서울역05:00818262-9.70000.00001.9000720.000010,805.00001634.5257
12021-01-011서울역06:00111352111-9.70000.00002.0000750.000010,805.000046312.8552
22021-01-011서울역07:00154434129-9.30000.00001.6000710.000010,805.000058816.3258
32021-01-011서울역08:00301583113-9.30000.00001.6000720.000010,805.000088424.5442
42021-01-011서울역09:00322833200-8.60000.00002.5000740.000010,805.0000115532.0685
52021-01-011서울역10:00411740268-6.10000.00001.1000680.000010,805.0000115131.9574
62021-01-011서울역11:00575623306-3.20000.00001.7000550.000010,805.0000119833.2624
72021-01-011서울역12:00734833331-1.10000.00002.1000520.000010,805.0000156743.5076
82021-01-011서울역13:00693784348-0.20000.00003.0000560.000010,805.0000147741.0088
92021-01-011서울역14:006697843161.40000.00001.6000530.000010,805.0000145340.3424
날짜호선명역명시간승차인원하차인원우대권인원수기온(°C)강수량(mm)풍속(m/s)습도(%)적설(cm)면적승하차인원혼잡도
56341362023-12-318남위례14:002652201032.60000.00002.5000923.30004,177.540048534.8291
56341372023-12-318남위례15:002922291303.80000.00002.7000903.00004,177.540052137.4144
56341382023-12-318남위례16:00309272964.30000.00002.2000852.60004,177.540058141.7231
56341392023-12-318남위례17:00260274544.00000.00002.2000862.50004,177.540053438.3479
56341402023-12-318남위례18:00209293783.10000.00000.7000892.50004,177.540050236.0499
56341412023-12-318남위례19:00121254512.60000.00000.8000902.40004,177.540037526.9297
56341422023-12-318남위례20:00135204441.90000.00002.2000942.40004,177.540033924.3445
56341432023-12-318남위례21:00228182451.70000.00002.4000962.40004,177.540041029.4432
56341442023-12-318남위례22:00178187271.30000.00000.6000952.40004,177.540036526.2116
56341452023-12-318남위례23:0094230240.90000.00000.8000962.40004,177.540032423.2673